Token Streaming

Token streaming delivers LLM output incrementally as tokens are generated, rather than waiting for the complete response, enabling real-time display in chat interfaces and reducing perceived latency.

Why It Matters

Without streaming, users wait for the entire response before seeing any output. For long responses, this can mean 10-30 seconds of loading. Token streaming solves this:

• Immediate feedback: Users see output within milliseconds of starting generation
• Better UX: Typing effect feels more natural and engaging
• Cancellation: Users can stop generation mid-response if it’s going wrong
• Progress indication: Visible progress instead of loading spinners

For AI engineers, streaming is essential for any user-facing LLM application. Users expect the ChatGPT-style typing experience.

Implementation Basics

Token streaming uses Server-Sent Events (SSE) or WebSockets to push tokens to the client. Each chunk contains one or more tokens with metadata.

Python example with OpenAI:

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain RAG"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Frontend handling:

1. Use EventSource or fetch with ReadableStream
2. Parse each SSE chunk and append to display
3. Handle the [DONE] signal for completion
4. Implement abort controller for cancellation

Considerations:

• Streaming adds complexity to error handling
• Function calls arrive as accumulated chunks
• Token counting requires reassembling the full response
• Some frameworks (Vercel AI SDK) abstract streaming complexity

Token streaming is non-negotiable for production chat interfaces. Implement it from day one.